论文标题

PETAB-系统生物学中参数估计问题的互操作规范

PEtab -- interoperable specification of parameter estimation problems in systems biology

论文作者

Schmiester, Leonard, Schälte, Yannik, Bergmann, Frank T., Camba, Tacio, Dudkin, Erika, Egert, Janine, Fröhlich, Fabian, Fuhrmann, Lara, Hauber, Adrian L., Kemmer, Svenja, Lakrisenko, Polina, Loos, Carolin, Merkt, Simon, Müller, Wolfgang, Pathirana, Dilan, Raimúndez, Elba, Refisch, Lukas, Rosenblatt, Marcus, Stapor, Paul L., Städter, Philipp, Wang, Dantong, Wieland, Franz-Georg, Banga, Julio R., Timmer, Jens, Villaverde, Alejandro F., Sahle, Sven, Kreutz, Clemens, Hasenauer, Jan, Weindl, Daniel

论文摘要

基于数据的建模研究结果的可重复性和可重复性至关重要。然而,到目前为止,还没有针对系统生物学中参数估计问题的规范的广​​泛支持格式。在这里,我们介绍了PAPAB,一种格式,该格式使用系统生物学标记语言(SBML)模型以及一组Tab分隔的值文件来促进参数估计问题的规范,这些文件描述了观察模型和实验数据以及要估计的参数。我们已经在八个公认的模型仿真和参数估计工具箱中实现了PAB支持,总共数百个用户。我们提供了一个Python库,用于验证和修改PAPAB问题,目前基于最近的研究为20个示例参数估计问题。 PETAB,PETAB PYTHON库的规格以及示例的链接以及所有支持软件工具的链接,请访问https://github.com/petab-dev/petab,可在https://doi.org/10.5281/zenodo.37322958上找到快照。所有原始内容均可在允许许可下获得。

Reproducibility and reusability of the results of data-based modeling studies are essential. Yet, there has been -- so far -- no broadly supported format for the specification of parameter estimation problems in systems biology. Here, we introduce PEtab, a format which facilitates the specification of parameter estimation problems using Systems Biology Markup Language (SBML) models and a set of tab-separated value files describing the observation model and experimental data as well as parameters to be estimated. We already implemented PEtab support into eight well-established model simulation and parameter estimation toolboxes with hundreds of users in total. We provide a Python library for validation and modification of a PEtab problem and currently 20 example parameter estimation problems based on recent studies. Specifications of PEtab, the PEtab Python library, as well as links to examples, and all supporting software tools are available at https://github.com/PEtab-dev/PEtab, a snapshot is available at https://doi.org/10.5281/zenodo.3732958. All original content is available under permissive licenses.

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